code: def vis_dataset_thumbnail(
      self, feature, *, num_mult=1, expand_mult=1, max_rep=None
  ):
      if max_rep is None:
          max_rep = num_mult
      if self.acts_reduced.shape[0] < num_mult**2:
          raise RuntimeError(
              f"At least {num_mult ** 2} observations are required to produce"
              " a thumbnail visualization."
          )
      acts_feature = self.acts_reduced[..., feature]
      pos_indices = argmax_nd(
          acts_feature,
          axes=[1, 2],
          max_rep=max_rep,
          max_rep_strict=True,
      )
      acts_single = acts_feature[
          range(acts_feature.shape[0]), pos_indices[0], pos_indices[1]
      ]
      obs_indices = np.argsort(-acts_single, axis=0)[: num_mult**2]
      coords = np.array(
          list(zip(*pos_indices)), dtype=[("h", int), ("w", int)]
      )[obs_indices]
      indices_order = np.argsort(coords, axis=0, order=("h", "w"))
      indices_order = indices_order.reshape((num_mult, num_mult))
      for i in range(num_mult):
          indices_order[i] = indices_order[i][
              np.argsort(coords[indices_order[i]], axis=0, order="w")
          ]
      obs_indices = obs_indices[indices_order]
      poses = np.array(pos_indices).transpose()[obs_indices] + 0.5
      self.pad_obses(expand_mult=expand_mult)
      patches = []
      patch_acts = np.zeros((num_mult, num_mult))
      patch_shapes = []
      for i in range(num_mult):
          patches.append([])
          for j in range(num_mult):
              obs_index = obs_indices[i, j]
              pos_h, pos_w = poses[i, j]
              patch = self.get_patch(
                  obs_index, pos_h, pos_w, expand_mult=expand_mult
              )
              patches[i].append(patch)
              patch_acts[i, j] = acts_single[obs_index]
              patch_shapes.append(patch.shape)
      patch_acts_max = patch_acts.max()
      opacities = patch_acts / (
          1 if patch_acts_max == 0 else patch_acts_max
      )
      patch_min_h = np.array([s[0] for s in patch_shapes]).min()
      patch_min_w = np.array([s[1] for s in patch_shapes]).min()
      for i in range(num_mult):
          for j in range(num_mult):
              opacity = opacities[i, j][None, None, None]
              opacity = opacity.repeat(patches[i][j].shape[0], axis=0)
              opacity = opacity.repeat(patches[i][j].shape[1], axis=1)
              patches[i][j] = np.concatenate(
                  [patches[i][j], opacity], axis=-1
              )
              patches[i][j] = patches[i][j][:patch_min_h, :patch_min_w]
      return (
          np.concatenate(
              [
                  np.concatenate(patches[i], axis=1)
                  for i in range(len(patches))
              ],
              axis=0,
          ),
          obs_indices.tolist(),
      )
  
file_path: lucid/lucid/scratch/rl_util/nmf.py
imports: import numpy
  from functools import reduce
  import scipy.ndimage
  from lucid.misc.channel_reducer import ChannelReducer
  import lucid.optvis.param
  import lucid.optvis.objectives
  import lucid.optvis.render
  import lucid.optvis.transform
  from .attribution import get_acts, get_attr
label_desc: Use image node_id 6 for calling the LayerNMF obj's underlying member method code with example usage: obj.vis_dataset_thumbnail(feature) and returns: np,  obs_indices